1. Technical Field
The present invention relates to an image processing apparatus and an image processing method for reducing noise on an image, and in particular, to an image processor and an image processing method for reducing noise from various digital images, such as medical digital images acquired by medical modalities.
2. Related Art
It is general that various types of noise are mixed on images acquired based on natural events. This noise is often given a generic name, called “image noise.” For higher-quality images, how to remove or reduce the image noise always lies as a significant technical subject.
In particular, image noise mainly made up of high-frequency signal components often exists on an image. In such an image, the image noise frequently becomes an obstacle to viewing structural objects to be targeted on an image (in medical images, the objects are for example born portions) under a superior visibility and a higher density resolution. That is, the visibility and density resolution are deteriorated badly. For example, for viewing a medical image, a deterioration in the visibility and/or density resolution makes it difficult to find out a tumor in soft tissue.
As reducing image noise, there have been known a technique for using a smoothing filter, a technique for making use of a statistical characteristic inherent to noise, and a technique for adjusting a gain in a frequency space.
However, the current situation is that the above image noise reduction techniques have still been poor in achieving a satisfactory noise reduction.
For instance, in cases where the technique for using a smoothing filter is employed, it is possible to remove or reduce image noise made up of high frequency signal components, but there arises a problem that the spatial resolution is lowered as well. This problem is attributable to the fact that regions on the image, such as boundaries of structural objects, which include much high frequency signal components, are smoothed as well. That is, simply, the image gets “blurred,” which results in a situation that contradicts the noise reduction effect.
Further, using the statistical characteristic of noise faces a problem that it is difficult to detect the statistical characteristic in advance and it is also difficult to perform real-time processing for the noise reduction. On the other hand, the technique for adjusting the gain in the frequency space has a difficulty in selectively removing only noise. This difficulty leads easily to the appearance of artifacts on filtered images.